During the past decade, we have developed a series
of high-performance protein palmitoylation sites predictors. In 2006,
CSS-Palm 1.0 was designed by Zhou et al. as the first program for palmitoylation
site prediction (Zhou,
et al., 2006). With a clustering and scoring strategy
algorithm (CSS), the CSS-Palm 1.0 was able to accurately predict protein
palmitoylation site. Later, Ren et al. updated the CSS-Palm 1.0 into
CSS-Palm 2.0 by introducing a matrix mutation approach, which made considerable
improvements over the previous version in terms of prediction capacity
and efficiency (Ren,
et al., 2008). In 2011, CSS-Palm 3.0 was released
with application of Group-based Prediction System (GPS) algorithm (Xue,
et al., 2011). In recent years, experimentally identified
palmitoylation sites have been significantly expanded hence efforts
should be exerted on up-grading the performance of palmitoylation site
prediction. In 2013, CSS-Palm 4.0
was released, which include a fourth-generation of GPS algorithm and
the latest training data set containing 583 palmitoylation sites from
277 distinct proteins. Notably, in the fourth-generation GPS algorithm, we integrated Particle Swarm Optimize (PSO) to improve the convergence speed and training accuracy.
To evaluate the prediction performance and system
robustness of CSS-Palm 4.0, the leave- one-out
validation and 4-, 6-, 8-, 10-fold cross-validations were performed.
By comparison with our previous versions, the performance of CSS-Palm
4.0 was greatly improved. Finally, the standalone version of
CSS-Palm 4.0 was implemented in Java SE
6 with high speed. The
CSS-Palm 4.0 could predict out potential palmitoylation sites for ~1,000
proteins (with an average length of ~1000aa) within two minutes. Also, with PHP and JavaScript, the online version was developed.Taken
together, we proposed that the CSS-Palm 4.0 will be a great help for
experimentalists. The CSS-Palm 4.0 is freely available at: http://csspalm.biocuckoo.org
while the previous version is also provided.